Wireless communication systems using 2 or 3 antennas at the transmitter and one antenna at the receiver have been defined in the current standards. Systems using 4 to 8 antennas at the transmitter and 4 to 8 antennas at the receiver are being considered for standardization. Also, the maximum spectral efficiency reported to have been achieved in practical/prototype systems so far is often less than 10 bps/Hz.
Multiple-input multiple-output (MIMO) techniques have become popular in realizing transmit diversity and high data rates through the use of multiple transmit antennas in wireless communication terminals, references [1]-[6]. We consider large MIMO systems having tens of transmit and receive antennas in each communication terminal, which are of interest due to the high spectral efficiencies (of the order of tens to hundreds of bps/Hz) possible in such systems. The key challenges in realizing such large MIMO systems include low-complexity detection, channel estimation,
RF/IF technologies and communication terminal size to accommodate large number of antennas. There can be several large MIMO applications where lacing of large number of antennas need not be a major issue. An example of such an scenario is to provide high-speed backbone connectivity between base stations using large MIMO links, where large number of antennas can be placed at the backbone base stations.
The state-of-the art MIMO systems do not achieve the full potential of MIMO communications. Exploitation of large number of communication dimensions (e.g., large number of space dimensions in the case of V-BLAST multi-antenna systems, and large number of space and time dimensions in the case of Space-Time Coded multi-antenna systems) is essential in order to fully reap the MIMO potential. The issue with using large number of antennas is the very high detection complexities involved. For example, optimum ML detection of MIMO signals require complexities exponential in number of antennas, which are prohibitive even for tens of antennas. Even other detectors in the literature which attempt to achieve near-ML performance have complexities cubic or more in number of antennas, which are still prohibitive for tens and hundreds of antennas.
This disclosure addresses the issue of low-complexity detection in large MIMO systems. Recent approaches to low-complexity multi-user detection and MIMO detection involve application of techniques from belief propagation as in reference [7], neural networks in references [8], [9], [10], Markov Chain Monte-Carlo methods in references [11], [12], and probabilistic data association in references [13], [14], to name a few. Detectors based on these techniques have been shown to achieve an average per-bit complexity that is linear in number of users, while achieving near-ML performance in large multi-user CDMA system settings as in reference [8], [9], [14]. These powerful techniques are increasingly being adopted in MIMO detection. Recently, in reference [9],[10], the present disclosure presents a powerful Hopfield neural network based likelihood ascent search (LAS) algorithm for low-complexity large MIMO detection, where we showed that, in large MIMO systems having hundreds of antennas, the LAS detector achieves i) an uncoded bit error rate (BER) performance which is almost the same as the AWGN-only SISO (i.e., no fading) performance given by Q(√{square root over (SNR)}); this excellent performance is achieved with an average per-symbol complexity of just O(NtNr), and ii) a coded BER performance close to within 4.7 dB of the theoretical MIMO capacity using hard decision outputs from the LAS algorithm as input to the turbo decoder.
A limitation with the LAS algorithm disclosed in reference [9], [10] is that it achieves near-maximum likelihood (ML) performance only with hundreds of antennas. Placing hundreds of antennas can be difficult in communication terminals that have space constraints.